# dataset settings
dataset_type = 'deepir.SIRSTVOCDetSegDataset'
data_root = 'data/SIRSTdevkit/'

# Example to use different file client
# Method 1: simply set the data root and let the file I/O module
# automatically Infer from prefix (not support LMDB and Memcache yet)

# data_root = 's3://openmmlab/datasets/detection/segmentation/VOCdevkit/'

# Method 2: Use `backend_args`, `file_client_args` in versions before 3.0.0rc6
# backend_args = dict(
#     backend='petrel',
#     path_mapping=dict({
#         './data/': 's3://openmmlab/datasets/segmentation/',
#         'data/': 's3://openmmlab/datasets/segmentation/'
#     }))
backend_args = None

train_pipeline = [
    dict(type='LoadImageFromFile', backend_args=backend_args),
    dict(type='LoadAnnotations',
         with_bbox=True,
         with_seg=True,
         imdecode_backend='pillow'),
    dict(type='Resize', scale=(512, 512), keep_ratio=False),
    dict(type='RandomFlip', prob=0.5),
    dict(type='PackDetInputs'),
    # dict(type='deepir.PrintPipeline'),
]
test_pipeline = [
    dict(type='LoadImageFromFile', backend_args=backend_args),
    dict(type='Resize', scale=(512, 512), keep_ratio=False),
    # avoid bboxes being resized
    dict(type='LoadAnnotations',
         with_bbox=True,
         with_seg=True,
         imdecode_backend='pillow'),
    dict(
        type='PackDetInputs',
        meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape',
                   'scale_factor'))
]
train_dataloader = dict(
    batch_size=2,
    num_workers=2,
    persistent_workers=True,
    sampler=dict(type='DefaultSampler', shuffle=True),
    batch_sampler=dict(type='AspectRatioBatchSampler'),
    dataset=dict(
        type='RepeatDataset',
        times=3,
        dataset=dict(
            type=dataset_type,
            data_root=data_root,
            ann_file='Splits/trainval_v2.txt',
            data_prefix=dict(sub_data_root=''),
            filter_cfg=dict(
                filter_empty_gt=False, min_size=0, bbox_min_size=0),
            pipeline=train_pipeline,
            backend_args=backend_args)
        ))

val_dataloader = dict(
    batch_size=1,
    num_workers=2,
    persistent_workers=True,
    drop_last=False,
    sampler=dict(type='DefaultSampler', shuffle=False),
    dataset=dict(
        type=dataset_type,
        data_root=data_root,
        ann_file='Splits/test_v2.txt',
        data_prefix=dict(sub_data_root=''),
        test_mode=True,
        pipeline=test_pipeline,
        backend_args=backend_args))
test_dataloader = val_dataloader

# Pascal VOC2007 uses `11points` as default evaluate mode, while PASCAL
# VOC2012 defaults to use 'area'.
val_evaluator = dict(type='VOCMetric', metric='mAP', eval_mode='area')
# val_evaluator = dict(type="VOCMetric", metric="mAP", eval_mode="11points")
test_evaluator = val_evaluator
